1. Field of the Invention
The present invention relates to methods and systems for automated and interactive processing of medical computer tomographic (CT) images, and is more specifically related to computerized methods and systems for multi-structure enhancement, volume matching, object analysis, and object detection in thoracic CT images using digital image processing techniques.
2. Background Art
Lung cancer has the highest cancer mortality for both men and women worldwide. Early detection and treatment of localized lung cancer at a potentially curable stage can significantly increase the patient survival rates. Studies have shown a survival rate of approximately 60% when lung cancer is detected in the early stages. However, only approximately 15% of lung cancers are diagnosed at an early stage when the disease is still localized.
Diagnosis of lung cancer can be accomplished using either projectional (i.e., chest radiography) or cross-sectional (i.e., computer tomography) techniques. Chest X-ray films have been used for lung cancer diagnosis as a conventional method for mass screening, due to their ready availability and reasonable sensitivity at showing localized lung abnormalities. However, there are obvious disadvantages inherent in the use of a projection image, the most notable being the masking of nearly half of the lungs by overlaying structures such as the heart and diaphragm.
Computer tomography (CT) provides a cross-sectional image of the lung, as opposed to the projection image provided by a chest X-ray. Since the early 1990s, the volumetric CT technique has been available to provide virtually contiguous spiral scans that cover the chest in a few seconds. This technique has greatly reduced CT image artifacts caused by unequal respiratory cycles, partial volume, and cardiac motion. Newer models of the helical CT system are capable of performing the scan and image reconstruction simultaneously. Detectability of pulmonary nodules has been greatly improved with this modality (Zerhouni et al., xe2x80x9cFactors Influencing Quantitative CT Measurements of Solitary Pulmonary Nodules,xe2x80x9d J. Comput. Assisted Tomography, 6:1075-87, 1982; Siegelman et al., xe2x80x9cSolitary Pulmonary Nodules: CT Assessment,xe2x80x9d Radiology, 160:307-312, 1986; Zerhouni et al., xe2x80x9cCT of Pulmonary Nodule: A Cooperative Study,xe2x80x9d Radiology, 160:319-327, 1986; Webb, W. R., xe2x80x9cRadiologic Evaluation of the Solitary Pulmonary Nodule,xe2x80x9d Am. J. Roentgenology, 154:701-708, 1990). High-resolution CT has also proved to be effective in characterizing the edges of pulmonary nodules (Zwirewich et al., xe2x80x9cSolitary Pulmonary Nodule: High-Resolution CT and Radiologic-Pathologic Correlation,xe2x80x9d Radiology, 79:469-476, 1991).
Recent studies have demonstrated that spiral CT can detect small lung nodules that are barely visible on chest X-ray films (Henschke et al., xe2x80x9cEarly Lung Cancer Action Project: Overall Design and Findings from Baseline Screening,xe2x80x9d The Lancet, Vol. 354, pp. 99-105, 1999; Sone et al., xe2x80x9cMass Screening for Lung Cancer with Mobil Spiral Computed Tomography Scanner,xe2x80x9d The Lancet, Vol. 351, pp. 1242-45, 1998). Zwirewich and his colleagues reported that shadows of nodule spiculation correlate pathologically with irregular fibrosis, localized lymphatic spread of a tumor, or an infiltrative tumor growth; pleural tags represent fibrotic bands that usually are associated with juxtacicatrical pleural retraction; and low attenuation bubble-like patterns are correlated with bronchioloalveolar carcinomas. These are common CT image patterns associated with malignant processes of lung masses.
Although skilled pulmonary radiologists have a high degree of accuracy in diagnosis of lung cancer using advanced CT imaging technology, there remain challenges that can not be overcome using current methods of training or by attaining high levels of clinical skill and experience. These include the miss rate for detection of small pulmonary nodules, the detection of minimal interstitial lung disease, and the detection of changes in preexisting interstitial lung disease.
Because a majority of solitary pulmonary nodules (SPN) are benign, Siegelman et al. (1986) determined three main criteria for benignancy: (a) high attenuation values distributed diffusely throughout the nodule, (b) a representative CT number of at least 164 HU, and (c) hamartomas, which are lesions 2.5 cm or less in diameter with sharp and smooth edges and a central focus of fat with CT numbers of xe2x88x9240 to xe2x88x92120 HU. These reports suggest that these are features that a computer-aided analytical tool could use to differentiate benign from malignant lesions. As to detection of lung cancers by various modes of CT, Remy-Jardin et al. (Remy-Jardin et al., xe2x80x9cPulmonary Nodules: Detection with Thick-Section Spiral CT versus Conventional CT,xe2x80x9d Radiology, 187:513-520, 1993) showed that the thick-section (10 mm) helical CT markedly reduces cardiac motion artifacts and misses fewer lung nodules than the conventional CT. In Japan, CT-based lung cancer screening programs have been developed (Tateno et al., xe2x80x9cDevelopment of X-ray CT for Lung Cancer Detection,xe2x80x9d Shih-Iryo, 17(10):28-32, 1990; Iinuma et al., xe2x80x9cBasic Idea of Lung Cancer Screening CT (LSCT) and Its Preliminary Evaluation,xe2x80x9d Jap. J. Radiol. Med., 52(2):182-190, 1992). In the U.S., however, only a limited demonstration project, funded by the NIH/NCI, using helical CT has been reported (Yankelevitz et al., xe2x80x9cRepeat CT Scanning for Evaluation of Small Pulmonary Nodules,xe2x80x9d Radiology, 1999). The trend towards using helical CT as one of the clinical tools for screening lung cancer has four motivating factors: (1) an alternative to the low sensitivity of chest radiography in the detection of small cancers (smaller than 10 mm); (2) the development of higher throughput, low-dose helical CT; (3) the potential reduction of health care costs using helical CT; and (4) the development of a computer diagnostic system as an aid for pulmonary radiologists. One can anticipate that the cost of each CT examination will still be higher than that of a conventional chest X-ray. However, for the high-risk population, the greater potential of this imaging modality for detecting early lung cancer may outweigh its increased cost.
Several clinical trials are now underway in the U.S. Studies at the Mayo Clinic and the University of South Florida were funded in 1999 and have recently begun. The method is being shown to detect small volume lung cancers, but with false positive rates for nodule detection in the range of 23-50%. In Japan, direct questioning of Drs. Sone and Kaneko (Oct. 1-3, 1999, Conference on Screening for Lung Cancer, Cornell Medical College, New York) indicated that the false negative rate in the work they were reporting was 10 to 15% and sometimes as high as 90% when the lesion is very small. In addition, Dr. Sone reported at that meeting that 16% of those patients undergoing thoracotomy had non-cancerous lesions and an additional 6% had atypical adenomatous hyperplasia, a benign lesion of uncertain malignant potential.
Given the frequency of false positive detections in both the Japanese and U.S. studies, the frequency of false negative exams in Japanese studies, and the frequency of thoracotomy uncovering only benign disease, there is a pressing need for development of improved diagnostic methods, as the use of screening CT is rapidly increasing and high false positive rates will result in many unnecessary procedures. Computer algorithms have been shown to increase sensitivity and/or specificity in the detection of lung cancer on chest radiographs and breast cancer on mammograms. Thus, application of these methods to screening CT is appropriate.
U.S. Pat. No. 6,125,194, filed Feb. 4, 1998, having the same assignee as the present application and incorporated herein by reference, is aimed specifically at identification of the smallest of lung nodules, 3 to 20 mm, well within the size limits for T1 cancer ( less than 30 mm). If there is to be a measurable benefit in the use of the helical CT, it is likely to be in detection of cancers at the lower limits of size detectable by radiologists, at or just below the radiographically detectable size.
An object of the present invention is to provide a computerized method and system for improving diagnostic performance for thoracic CT images.
Advantageously, the invention provides a fast method of assisting the radiologist in the comparison of two CT images that were scanned at different times. The radiation dosage of two image scans used for either prospective or retrospective comparison can be different. That is, either low-dose or high-resolution CT images or a combination of the two can be used.
The invention provides a method of segmenting the lung field. Based on the segmented lung area, this invention also provides a way to enhance both the lung and the mediastinum histograms separately (for multi-structure enhancement).
The invention additionally provides a method of further segmenting the region of interest (within the lung) from 2D slices and thus to reconstruct the 3D objects (such as vessels, bronchi, or nodules). The intrinsic (e.g., intensity) and extrinsic (e.g., geometric) properties of these extracted 3D objects can then be used for the feature analysis.
According to a further aspect of the invention, there is provided a method to compute parameters of the features associated with nodules and cancers. The features include the size, sphericity, speculation, and boundary of the suspected nodule or cancer.
Advantageously, the method of the invention allows for detecting solitary lung nodules. Based on the extracted features from previous steps, one can use an artificial neural network, fuzzy-logic, or rule-based classifiers to distinguish nodules from other objects such as vessels or chest walls.